Building Intelligent
Autonomous Architectures
I'm Vincenzo Barbuto, a Research Fellow at DIMES, University of Calabria, Italy. Exploring the frontiers of Edge AI, Digital Twins, and Cyber-Physical Systems.
About Me
My academic journey and research interests.
My research focuses on Edge Intelligence, an emerging field at the intersection of artificial intelligence (AI), the Internet of Things (IoT), and edge computing. Specifically, I investigate intelligent devices and systems capable of processing data locally, reducing reliance on cloud-based services. My goal is to understand how these devices can seamlessly integrate into complex cyber-physical systems, ranging from intelligent traffic monitoring to emergency vehicle detection. Beyond Edge Intelligence, I am also interested in Digital Twins, digital counterparts of physical systems.
Research Interests
- Edge Intelligence / Edge AI
- Digital Twin
- Internet of Things
- Cyber-Physical Systems
Education
PhD in ICT
University of Calabria
Visiting Student Researcher
University of California, Berkeley
MSc Computer Eng. for the IoT
University of Calabria
MSc Data Sci. & Network Intel.
Télécom SudParis
BSc Computer Engineering
University of Calabria
Core Technologies
Featured Project
A deep dive into one of my key research activities.
Recent Publications
My latest research on Edge AI and Digital Twins.
184citations5h-index4i10-index
Wearable Monitoring for Early Cardiotoxicity Detection in Cancer Patients: The COMPASS Vision
Conference Paper2026 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv)2026Cancer therapies have substantially improved survival outcomes, yet cancer therapy-related cardiovascular toxicity (CTR-CVT) remains a major concern, with approximately one in four anticancer drugs carrying cardiac or vascular safety warnings. Since current monitoring relies on periodic hospital-based assessments, transient or progressively evolving cardiovascular alterations often escape timely detection. Within the context of COMPASS (Cardio-Oncology Multidisciplinary Patient Assistance Solution), an EU Innovative Health Initiative (IHI) project (Grant Agreement No. 101253264), this extended abstract explores continuous wearable sensing in real-world environments as a complementary paradigm for early detection of CTR-CVT. After reviewing the clinical indicators most relevant to longitudinal monitoring, we survey the corresponding wearable modalities together with their typical accuracy ranges and metrological limitations, and introduce the COMPASS research vision integrating wearable-derived data into AI-based clinical decision support for personalized cardio-oncology care.
Wearable MonitoringCardiotoxicityeHealthCOMPASS VisionRead paperNamed Data Networking (NDN) for Collective Network Intelligence: a Smart Museum case study
Conference Paper2026 22nd International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)2026Future smart systems are increasingly characterized by pervasive AI deployed both at the back- and front-end. However, limiting intelligence to the software layer is insufficient to meet the scalability, adaptability, and resilience requirements of emerging large-scale cyber-physical ecosystems. Indeed, intelligence must extend across both the entire architectural stack and the infrastructure, including the network layer itself. In this paper, we explore Named Data Networking (NDN) as an enabler of collective intelligence at the network level. Unlike traditional IP-based architectures, NDN natively supports in-network caching, name-based routing, and stateful forwarding, thus enabling distributed decision-making mechanisms to emerge directly from the network fabric. We argue that these properties can be interpreted as forms of collective intelligence, where the network collaboratively optimizes content dissemination and resource utilization, and validate our findings through a Smart Museum case study.
Named Data NetworkingCollective IntelligenceIoTSmart SystemsRead paperUncertainty-Aware Digital Twins for Industry 4.0: A Metrology-Driven Conceptual Framework with a Healthcare Robotics Case Study
Conference Paper2026 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT)2026Digital twins have emerged as a key enabling technology in Industry 4.0, enabling virtual representations of physical systems that evolve in real time through data synchronization. They are widely applied to industrial robotic systems such as manipulators, collaborative robots, and automated production cells for applications including process monitoring, predictive maintenance, and control optimization. Despite their growing adoption, many existing digital twin implementations implicitly assume that sensor measurements are accurate and reliable. In practice, measurements of key variables such as position, velocity, force, and system states are affected by uncertainty arising from noise, bias, drift, and environmental disturbances, which can significantly impact the reliability of digital twin predictions. This paper proposes a metrology-driven conceptual framework for uncertainty-aware digital twins (UA-DTs), integrating measurement uncertainty into the digital twin lifecycle to ensure trustworthy monitoring and control, exemplified through a healthcare robotics case study.
Digital TwinMetrologyIndustry 4.0RoboticsRead paper
Recent Talks & News
Latest presentations and conference talks.
- September 2026
An Edge-Cloud Digital Twin Platform for Intelligent Smart Park Services
The 2026 IEEE International Conference on Digital Twin (IEEE Digital Twin 2026)Protected natural areas increasingly require intelligent infrastructures to monitor environmental conditions, support visitors, and assist park management while preserving privacy and operating under limited connectivity. Addressing these challenges requires integrated architectures that combine distributed sensing, edge intelligence, cloud services, and Digital Twin (DT) technologies. This work presents a Smart Park platform developed for the "I Giganti della Sila" State Reserve in the Sila National Park, Calabria, Italy. The platform adopts a layered edge-cloud architecture organized around a closed operational loop that acquires environmental and visitor data, processes them through distributed intelligence, and delivers actionable services to visitors and park operators. A territorial DT federates multiple sensing and analytics subsystems into a unified, real-time representation of the park.
- September 2026
Digital Twin Engineering: Architectures, AI Techniques, and the TopDown/BottomUp Lifecycle of Behavior Model Construction
Tutorial @ 2026 IEEE Smart World Congress (IEEE SWC 2026)Tutorial co-presented with Dr. Roberto Minerva exploring the software-oriented foundations of Digital Twin engineering. It examines how architectural choices, AI techniques (Deep Learning, Generative AI, and Reinforcement Learning in the loop), and top-down/bottom-up methodologies shape viable behavior models across the entire lifecycle of a Twin.